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Zhang, Jun

Publications and source records attributed to Zhang, Jun.

At least 37 records · Page 2

Modulating Electronic Structure of Atomically Dispersed Nickel Sites through Boron and Nitrogen Dual Coordination Boosts Oxygen Reduction

Abstract Atomically dispersed 3D transitional metal active sites with nitrogen coordination anchored on carbon support have emerged as a kind of promising electrocatalyst toward oxygen reduction reaction (ORR) in the field of fuel cells and metal–air cells. However, it is still a challenge to accurately modulate the coordination structure of single‐atom metal sites, especially first‐shell coordination, as well as identify the relationship between the geometric/electronic structure and ORR performance. Herein, a carbon‐supported single‐atom nickel catalyst is fabricated with boron and nitrogen dual coordination (denoted as Ni‐B/N‐C). The hard X‐ray absorption spectrum result reveals that atomically dispersed Ni active sites are coordinated with one B atom and three N atoms in the first shell (denoted as Ni‐B 1 N 3 ). The Ni‐B/N‐C catalyst exhibits a half‐wave potential ( E 1/2 ) of 0.87 V versus RHE, along with a distinguished long‐term durability in alkaline media, which is superior to commercial Pt/C. Density functional theory calculations indicate that the Ni‐B 1 N 3 active sites are more favorable for the adsorption of ORR intermediates relative to Ni‐N 4 , leading to the reduction of thermodynamic barrier and the acceleration of reaction kinetics, which accounts for the increased intrinsic activity.

Chemistry↗

Activating Single‐Atom Ni Site via First‐Shell Si Modulation Boosts Oxygen Reduction Reaction

Abstract Atomically dispersed nitrogen‐coordinated 3d transition‐metal site on carbon support (M‐NC) are promising alternatives to Pt group metal‐based catalysts toward oxygen reduction reaction (ORR). However, despite the excellent activities of most of M‐NC catalysts, such as Fe‐NC, Co‐NC et al., their durability is far from satisfactory due to Fenton reaction. Herein, this work reports a novel Si‐doped Ni‐NC catalyst (Ni‐SiNC) that possesses high activity and excellent stability. X‐ray absorption fine structure and aberration‐corrected transmission electron microscopy uncover that the single‐atom Ni site is coordinated with one Si atom and three N atoms, constructing Ni‐Si 1 N 3 moiety. The Ni‐SiNC catalyst exhibits a half‐wave potential (E 1/2 ) of 0.866 V versus RHE, with a distinguished long‐term durability in alkaline media of only 10 mV negative shift in E 1/2 after 35 000 cycles, which is also validated in Zn‐air battery. Density functional theory calculations reveal that the Ni‐Si 1 N 3 moiety facilitates ORR kinetics through optimizing the adsorption of intermediates.

Chemistry↗

Super‐Hydrophilic Microporous Ni(OH) x /Ni 3 S 2 Heterostructure Electrocatalyst for Large‐Current‐Density Hydrogen Evolution

Abstract Exploiting active and stable non‐precious metal electrocatalysts for alkaline hydrogen evolution reaction (HER) at large current density plays a key role in realizing large‐scale industrial hydrogen generation. Herein, a self‐supported microporous Ni(OH) x /Ni 3 S 2 heterostructure electrocatalyst on nickel foam (Ni(OH) x /Ni 3 S 2 /NF) that possesses super‐hydrophilic property through an electrochemical process is rationally designed and fabricated. Benefiting from the super‐hydrophilic property, microporous feature, and self‐supported structure, the electrocatalyst exhibits an exceptional HER performance at large current density in 1.0 M KOH, only requiring low overpotential of 126, 193, and 238 mV to reach a current density of 100, 500, and 1000 mA cm −2 , respectively, and displaying a long‐term durability up to 1000 h, which is among the state‐of‐the‐art non‐precious metal electrocatalysts. Combining hard X‐rays absorption spectroscopy and first‐principles calculation, it also reveals that the strong electronic coupling at the interface of the heterostructure facilitates the dissociation of H 2 O molecular, accelerating the HER kinetics in alkaline electrolyte. This work sheds a light on developing advanced non‐precious metal electrocatalysts for industrial hydrogen production by means of constructing a super‐hydrophilic microporous heterostructure.

Chemistry↗

Shear Measurement with Poorly Resolved Images

Weak lensing studies typically require excellent seeing conditions for the purpose of maximizing the number density of well-resolved galaxy images. It is interesting to ask to what extent the seeing size limits the usefulness of the astronomical images in weak lensing. In this work, we study this issue with the data of the DECam Legacy Survey, which is a part of the target selection program for the Dark Energy Spectroscopic Instrument. Using the Fourier_Quad shear measurement pipeline, we demonstrate that images with relatively poor seeing conditions (~1$^"_.$5) can still yield accurate shear estimators. We do not find any correlation between systematic shear error and the image resolution.

79 ASTRONOMY AND ASTROPHYSICS↗

Massive star-forming galaxies have converted most of their halo gas into stars

In the local Universe, the efficiency for converting baryonic gas into stars is very low. In dark matter halos where galaxies form and evolve, the average efficiency varies with galaxy stellar mass and has a maximum of about 20% for Milky-Way-like galaxies. The low efficiency at higher mass is believed to be the result of some quenching processes, such as the feedback from active galactic nuclei. We perform an analysis of weak lensing and satellite kinematics for SDSS central galaxies. Our results reveal that the efficiency is much higher, more than 60%, for a large population of massive star-forming galaxies around 10 11 M ⊙ . This suggests that these galaxies acquired most of the gas in their halos and converted it into stars without being significantly affected by quenching processes. This population of galaxies is not reproduced in current galaxy formation models, indicating that our understanding of galaxy formation is incomplete. The implications of our results on circumgalactic media, star-formation quenching, and disk galaxy rotation curves are discussed. We also examine systematic uncertainties in halo-mass and stellar-mass measurements that might influence our results.

79 ASTRONOMY AND ASTROPHYSICS↗

Superconductivity above 200 K discovered in superhydrides of calcium

Searching for superconductivity with T c near room temperature is of great interest both for fundamental science & many potential applications. Here we report the experimental discovery of superconductivity with maximum critical temperature (T c ) above 210 K in calcium superhydrides, the new alkali earth hydrides experimentally showing superconductivity above 200 K in addition to sulfur hydride & rare-earth hydride system. The materials are synthesized at the synergetic conditions of 160~190 GPa and ~2000 K using diamond anvil cell combined with in-situ laser heating technique. The superconductivity was studied through in-situ high pressure electric conductance measurements in an applied magnetic field for the sample quenched from high temperature while maintained at high pressures. The upper critical field Hc(0) was estimated to be ~268 T while the GL coherent length is ~11 Å. The in-situ synchrotron X-ray diffraction measurements suggest that the synthesized calcium hydrides are primarily composed of CaH 6 while there may also exist other calcium hydrides with different hydrogen contents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

First measurement of the characteristic depletion radius of dark matter haloes from weak lensing

ABSTRACT We use weak lensing observations to make the first measurement of the characteristic depletion radius, one of the three radii that characterize the region where matter is being depleted by growing haloes. The lenses are taken from the halo catalogue produced by the extended halo-based group/cluster finder applied to DESI Legacy Imaging Surveys DR9, while the sources are extracted from the DECaLS DR8 imaging data with the fourier_quad pipeline. We study halo masses $12 \lt \log (M_{\rm grp} \,[{\rm M_{\odot }}\, h^{-1}]) \le 15.3$ within redshifts 0.2 ≤ z ≤ 0.3. The virial and splashback radii are also measured and used to test the original findings on the depletion region. When binning haloes by mass, we find consistency between most of our measurements and predictions from the cosmicgrowth simulation, with exceptions to the lowest mass bins. The characteristic depletion radius is found to be roughly 2.5 times the virial radius and 1.7–3 times the splashback radius, in line with an approximately universal outer density profile, and the average enclosed density within the characteristic depletion radius is found to be roughly 29 times the mean matter density of the Universe in our sample. When binning haloes by both mass and a proxy for halo concentration, we do not detect a significant variation of the depletion radius with concentration, on which the simulation prediction is also sensitive to the choice of concentration proxy. We also confirm that the measured splashback radius varies with concentration differently from simulation predictions.

79 ASTRONOMY AND ASTROPHYSICS↗

Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control

Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.

deep reinforcement learning↗

Poly( N -vinylpyrrolidone)- block -Poly(dimethylsiloxane)- block -Poly( N -vinylpyrrolidone) Triblock Copolymer Polymersomes for Delivery of PARP1 siRNA to Breast Cancers

Nearly 20% of HER2-positive breast cancers develop resistance to HER2-targeted therapies requiring the use of advanced therapies. Silencing RNA therapy may be a powerful modality for treating resistant HER2 cancers due to its high specificity and low toxicity. However, the systemic administration of siRNAs requires a safe and efficient delivery platform because of siRNA’s low stability in physiological fluids, inefficient cellular uptake, immunoreactivity, and rapid clearance. We have developed theranostic polymeric vesicles to overcome these hurdles for encapsulation and delivery of small functional molecules and PARP1 siRNA for in vivo delivery to breast cancer tumors. The 100 nm polymer vesicles were assembled from biodegradable and non-ionic poly(N-vinylpyrrolidone) 14 -block-poly(dimethylsiloxane) 47 -block-poly(N-vinylpyrrolidone) 14 triblock copolymer PVPON 14 -PDMS 47 -PVPON 14 using nanoprecipitation and thin-film hydration. We demonstrated that the vesicles assembled from the copolymer covalently tagged with the Cy5.5 fluorescent dye for in vivo imaging could also encapsulate the model drug with high loading efficiency (40%). The dye-loaded vesicles were accumulated in tumors after 18 h circulation in 4TR breast tumor-bearing mice via passive targeting. We found that PARP1 siRNA encapsulated into the vesicles was released intact (13%) into solution by the therapeutic ultrasound treatment as quantified by gel electrophoresis. Additionally, the PARP1 siRNA-loaded polymersomes inhibited the proliferation of MDA-MB-361TR cells by 34% after 6 days of treatment by suppressing the NF-kB signaling pathway, unlike their scrambled siRNA-loaded counterparts. Finally, the treatment by PARP1 siRNA-loaded vesicles prolonged the survival of the mice bearing 4T1 breast cancer xenografts, with the 4-fold survival increase, unlike the untreated mice after 3 weeks following the treatment. These biodegradable, non-ionic PVPON 14 -PDMS 47 -PVPON 14 polymeric nanovesicles capable of the efficient encapsulation and delivery of PARP1 siRNA to successfully knock down PARP1 in vivo can provide an advanced platform for the development of precision-targeted therapeutic carriers, which could help develop highly effective drug delivery nanovehicles for breast cancer gene therapy.

60 APPLIED LIFE SCIENCES↗

Functionalization of Electrodes with Tunable [EMIM] x [Cl] x +1 – Ionic Liquid Clusters for Electrochemical Separations

Functionalization of electrodes with clusters of hydrophobic molecules may improve the energy efficiency and selectivity of electrochemical separations by modulating the desolvation process occurring at the interface. Ionic liquids (IL), which have a broad range of compositions and properties, are potential candidates for controlling the transport, desolvation, and adsorption of target ions at electrochemical interfaces. We report a joint experimental and theoretical investigation of the structure, stability, and selective adsorption properties of the IL clusters 1-ethyl-3-methylimidazolium chloride [EMIM] x [Cl] x+1 - (x = 1 – 10) and demonstrate their ability to adsorb and separate ions from solution. The structure and stability of the IL clusters are determined experimentally using high-mass-resolution electrospray ionization mass spectrometry, collision-induced dissociation, and negative ion photoelectron spectroscopy. Global optimization theory and ab initio molecular dynamics simulations provide molecular-level insight into the bonding and structural fluxionality of these species. Ion soft landing is used to selectively functionalize the surface of highly oriented pyrolytic graphite (HOPG) working electrodes with [EMIM] 1 [Cl] 2 - , [EMIM] 3 [Cl] 4 - , and [EMIM] 5 [Cl] 6 - clusters. Kelvin probe microscopy provides insight into the relative stability of the clusters on HOPG and their effect on the work function of IL-functionalized electrodes. Cyclic voltammetry measurements reveal irreversible adsorption of Fe(CN) 6 4-/3- anions during redox cycling, while electrochemical impedance spectroscopy indicates a substantial decrease in the electron transfer resistance of the IL-functionalized electrodes due to adsorption of Fe(CN) 6 4-/3- . Overall, our findings demonstrate that IL clusters with different size and stoichiometry may be used to increase the efficiency of electrochemical separations, opening new horizons in selective electrode functionalization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019

Abstract This article presents a hydrological reconstruction of the Upper Colorado River Basin with an hourly temporal resolution, and 1-km spatial resolution from October 1982 to September 2019. The validated dataset includes a suite of hydrologic variables including streamflow, water table depth, snow water equivalent (SWE) and evapotranspiration (ET) simulated by an integrated hydrological model, ParFlow-CLM. The dataset was validated over the period with a combination of point observations and remotely sensed products. These datasets provide a long-term, natural-flow, simulation for one of the most over-allocated basins in the world.

54 ENVIRONMENTAL SCIENCES↗

Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19

The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems.

black swan event↗

Structural and functional insights into the first Bacillus thuringiensis vegetative insecticidal protein of the Vpb4 fold, active against western corn rootworm

The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, is a major maize pest in the United States causing significant economic loss. The emergence of field-evolved resistant WCR to Bacillus thuringiensis (Bt) traits has prompted the need to discover and deploy new insecticidal proteins in transgenic maize. In the current study we determined the crystal structure and mode of action (MOA) of the Vpb4Da2 protein (formerly known as Vip4Da2) from Bt, the first identified insecticidal Vpb4 protein with commercial level control against WCR. The Vpb4Da2 structure exhibits a six-domain architecture mainly comprised of antiparallel β-sheets organized into β-sandwich layers. The amino-terminal domains 1–3 of the protein share structural homology with the protective antigen (PA) PA14 domain and encompass a long β-pore forming loop as in the clostridial binary-toxB module. Domains 5 and 6 at the carboxyl-terminal half of Vpb4Da2 are unique as this extension is not observed in PA or any other structurally-related protein other than Vpb4 homologs. These unique Vpb4 domains adopt the topologies of carbohydrate-binding modules known to participate in receptor-recognition. Functional assessment of Vpb4Da2 suggests that domains 4–6 comprise the WCR receptor binding region and are key in conferring the observed insecticidal activity against WCR. The current structural analysis was complemented by in vitro and in vivo characterizations, including immuno-histochemistry, demonstrating that Vpb4Da2 follows a MOA that is consistent with well-characterized 3-domain Bt insecticidal proteins despite significant structural differences.

59 BASIC BIOLOGICAL SCIENCES↗

Blockchain Enabled Intelligence of Federated Systems (BELIEFS): An attack-tolerant trustable distributed intelligence paradigm

In this article, a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) is proposed to conduct cooperative control for the multi-regional large-scale power system with a multi-agents system (MAS). By establishing a two levels blockchain, each regional AI agent can simultaneously manage intra-regional controllers and cooperate with other AI agents. Under the consensus mechanism, the agents, which respectively conducted distributed deep reinforcement learning (DDRL) algorithm in multi-regions, can have the tolerant capability of malicious attacks in their training process. The demonstration of the proposed approach is within a multi-regional large-scale interconnected power system. Under the mode of "centralized dispatching and hierarchical management", this article aims to definite a mathematical model to deal with the control problem of the power systems. With the comparison experiments, the effectiveness and efficiency of our proposed method in the training process are verified. In addition, malicious attacks are set on the main chain and shard chains to verify the attack-tolerant capability. We expect that such approach and results can suggest a new paradigm of attack-tolerant trustable distributed AI deployment.

97 MATHEMATICS AND COMPUTING↗

Tailored computational approaches to interrogate heavy element chemistry and structure in condensed phase

In this chapter we are presenting a brief review of the challenges encountered in the study of 4f and 5f block elements in the condensed phase. Their recovery, use in molten salt reactors and other interesting applications necessitate the use of molecular dynamics and large-scale models that take into account both the electronic structure and relativistic corrections. Sampling the multitude of electronic and atomic configurational states is at the heart of reliable predictions of structure, reactivity, dynamics and transport of heavy metals in complex environments. We present three examples that combine lanthanide elements with large scale models: i) our recent developments of a versatile adaptive learning method that enables global optimization in high dimensional spaces, ii) results of computed pKa values of lanthanide aqua complexes, and iii) structure and computed EXAFS of heavy elements in molten salts. The latter two employ our recently-optimized lanthanide pseudo-potentials and companion basis sets for condensed phase ab initio molecular dynamics.

Nguyen, Manh Thuong↗